Method and apparatus for improving lag correction during in vivo measurement of analyte concentration with analyte concentration variability and range data

ABSTRACT

Methods, devices, and systems are provided for correcting lag in measurements of analyte concentration level in interstitial fluid. The invention includes receiving a signal representative of sensor data from an analyte monitoring system related to an analyte level measured over time, computing rates of change of the sensor data for a time period of the sensor data, computing a rate distribution of the rates of change, transforming the rate distribution into a linear arrangement, determining a best-fit line for the transformed rate distribution, computing a slope of the best-fit line; and using the slope of the best-fit line as a representation of a variability of the analyte level to adjust an amount of lag correction applied to the sensor data. Numerous additional features are disclosed.

PRIORITY

The present application is a continuation of U.S. patent applicationSer. No. 14/431,168 filed Mar. 25, 2015, now U.S. Pat. No. 9,907,492,which is a national stage patent application under 35 U.S.C. § 371,which claims priority to PCT Application No. PCT/US2013/060471 filedSep. 18, 2013, which claims priority to U.S. Provisional Application No.61/705,929 filed Sep. 26, 2012, entitled “Method and Apparatus forImproving Lag Correction During In Vivo Measurement of AnalyteConcentration with Analyte Concentration Variability and Range Data”,the disclosures of each of which are incorporated herein by reference intheir entirety for all purposes.

BACKGROUND

The detection of the concentration level of glucose or other analytes incertain individuals may be vitally important to their health. Forexample, the monitoring of glucose levels is particularly important toindividuals with diabetes or pre-diabetes. People with diabetes may needto monitor their glucose levels to determine when medication (e.g.,insulin) is needed to reduce their glucose levels or when additionalglucose is needed.

Devices have been developed for automated in vivo monitoring of analyteconcentrations, such as glucose levels, in bodily fluids such as in theblood stream or in interstitial fluid. Some of these analyte levelmeasuring devices are configured so that at least a portion of thedevices are positioned below a skin surface of a user, e.g., in a bloodvessel or in the subcutaneous tissue of a user. As used herein, the termanalyte monitoring system is used to refer to any type of in vivomonitoring system that uses a sensor disposed with at least a portionsubcutaneously to measure and store sensor data representative ofanalyte concentration levels automatically over time. Analyte monitoringsystems include both (1) systems such as continuous glucose monitors(CGMs) which transmit sensor data continuously or at regular timeintervals (e.g., once per minute) to a processor/display unit and (2)systems that transfer stored sensor data in one or more batches inresponse to a request from a processor/display unit (e.g., based on anactivation action and/or proximity, for example, using a near fieldcommunications protocol) or at a predetermined but irregular timeinterval.

Determining an analyte concentration level in blood based on the analyteconcentration in interstitial fluid can be difficult because changes ofthe analyte concentration levels in interstitial fluid typically lagsbehind changing analyte concentration levels in blood. Thus, what isneeded are systems, methods, and apparatus to correct for the time lagbetween blood analyte level changes and interstitial fluid analyte levelchanges.

SUMMARY

Methods, devices, and systems are provided for correcting time lag inmeasurements of analyte concentration level in interstitial fluid. Whenapplied to lag correction of glucose using analyte monitoring system(e.g., CGM) sensor data measuring glucose in interstitial fluid, thedegree of glycemic variability and/or range are used to determine therelative benefit of relying on the computed glucose rate of change forlag correction versus the risk of reduced precision caused by amplifyingnoise and other artifacts. Thus, in some embodiments, the inventionincludes determining the analyte concentration variability of a patientand/or the analyte concentration range of a patient and determining alag correction value to apply to sensor data representative of analyteconcentration measured in interstitial fluid using an analytemeasurement system. The lag correction value is adjusted based upon theanalyte concentration variability and/or analyte concentration range.Finally, an analyte concentration level representative of the bloodanalyte concentration level is computed based on the adjusted lagcorrection value. Related systems and computer program products are alsodisclosed.

In some embodiments, the invention includes receiving a signalrepresentative of sensor data from an analyte monitoring system relatedto an analyte level of a patient measured over time. Rates of change ofthe sensor data for a time period of the sensor data are computed alongwith a rate distribution of the rates of change. The rate distributionis transformed into a linear arrangement, a best-fit line is determinedfor the transformed rate distribution, a slope of the best-fit line iscomputed, and a scaling factor for lag correction is determined. Theslope of the best-fit line is used as a representation of thevariability of the analyte level to adjust an amount of lag correctionapplied to the sensor data by adjusting the scaling factor for lagcorrection. Related systems and computer program products are alsodisclosed.

Some other embodiments of the present disclosure includecomputer-implemented methods of correcting lag in measurements ofanalyte concentration level in interstitial fluid. The methods includedefining a scaling factor for lag correction, collecting a moving windowof historical analyte sensor data, defining a probability densityfunction of the sensor data within the moving window, determining anormalized analyte variability ratio, storing the normalized analytevariability ratio computed at regular intervals, comparing a latestnormalized analyte variability ratio to a predetermined value and anumber of prior values, setting a value of the scaling factor based onthe probability density function, and computing lag corrected valuesbased on the scaling factor. Related systems and computer programproducts are also disclosed.

Yet other embodiments of the present disclosure include additional andalternative methods of correcting lag in measurements of analyteconcentration level in interstitial fluid. The methods includedetermining at least one of analyte concentration variability of apatient and analyte concentration range, determining a lag correctionvalue to apply to sensor data representative of analyte concentrationmeasured in interstitial fluid using an analyte measurement system,adjusting the lag correction value based upon the at least one ofanalyte concentration variability and analyte concentration range, andcomputing an analyte concentration level representative of a bloodanalyte concentration level based on the adjusted lag correction value.Related systems and computer program products are also disclosed.

Numerous other aspects and embodiments are provided. Other features andaspects of the present invention will become more fully apparent fromthe following detailed description, the appended claims, and theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a plot of an example analyte concentration rate of changedistribution in accordance with some embodiments of the presentinvention.

FIG. 2 depicts a plot of an example transformed analyte concentrationrate of change distribution in accordance with some embodiments of thepresent invention.

FIG. 3 depicts a plot of example best-fit lines of a transformed analyteconcentration rate of change distribution in accordance with someembodiments of the present invention.

FIG. 4 depicts a flowchart illustrating an example of a method ofdetermining glucose variability in accordance with some embodiments ofthe present invention.

FIG. 5 depicts a flowchart illustrating an example of a method of lagcorrection based on glucose variability in accordance with someembodiments of the present invention.

FIG. 6 depicts a flowchart illustrating an example of a method ofmonitoring glycemic control based on glucose variability in accordancewith some embodiments of the present invention.

FIGS. 7A to 7C depict plots of example glucose levels over time,corresponding rate of change of the glucose levels over time, andbest-fit lines of the corresponding transformed glucose concentrationrate of change distribution, respectively and in accordance with someembodiments of the present invention.

FIG. 8 depicts a flowchart illustrating an example of a method of lagcorrection based on glucose range in accordance with some embodiments ofthe present invention.

DETAILED DESCRIPTION

The present invention provides systems, methods, and apparatus toimprove lag correction in devices that determine analyte concentrationin the blood via measurement of the analyte concentration ininterstitial fluid. For such devices, determining blood glucose levels,for example, may involve performing lag correction based on a calculatedestimate of rates of change of blood glucose levels. However, theaccuracy of computing the rates of change can be very sensitive tonoise. It has been observed that in patients with relatively goodglycemic control (i.e., relatively low blood glucose level variability),the relative performance improvement due to lag correction is not assignificant as in subjects with poorer control (i.e., relatively highblood glucose level variability). In some cases, the risk of reducedaccuracy due to rate calculation error increases because a higherfraction of the computed rate is due to noise and other artifacts.

Improving lag correction is thus a tradeoff between maximal smoothing(i.e., increasing precision) during periods of noisy, unchanging levelsand maximal lag correction (i.e., increasing accuracy) during periods ofnon-noisy, rapidly changing levels. Therefore, given a constant noiselevel, a relatively unchanging glucose level benefits from less lagcorrection than a relatively rapidly changing glucose level. Existingmethods of lag correction may rely on estimating the glucose level trendand minute-by-minute noise level to determine the amount of smoothing toapply. In contrast, the present invention uses information beyond thetime span in which the signals are still highly correlated, to get amore global sense of the patient's glucose level variability.

In some embodiments, the present invention considers rates of change ofglucose concentration levels based on glucose measurements over time andassesses the degree of glucose level variability that is relativelyinsensitive to noise and other artifacts. The degree of glucose levelvariability is usable in several ways. In some embodiments, the degreeof glucose level variability is used to help determine the amount oftradeoff between maximizing lag correction of interstitial glucosemeasurements and minimizing output noise. In some embodiments, thedegree of glucose level variability is being used to aid in measuring apatient's degree of glycemic control.

In addition to considering the rate of change of glucose levels,considering the range of a patient's glucose levels can also be used toimprove lag correction according to the present invention. The factorsthat reduce precision affect lag correction more at the extreme ends ofa patient's glucose excursion. For example, at the lower end of apatient's glucose levels, the levels can be affected by dropouts andother signal artifacts in a higher percentage than at the higher end. Inother words, a 30 mg/dL dropout at a 60 mg/dL glucose level is a 50%error while the same 30 mg/dL dropout at a 180 mg/dL level is only a 17%error. As a result, the risk of introducing error when lag correcting tothe full extent differs in these different glucose level ranges. Thus,considering the range of a patient's glucose levels and the patient'slevel patterns can be used to relate the risk of making a lag correctionand the factors that reduce precision.

Since a patient's glucose levels do not regularly follow a repetitivepattern and patients have different patterns that can change over time,a static plot of a patient's glucose response to a meal, for example, isnot likely to be useful for gauging the range of a patient's glucoselevels. However, by starting with conservative nominal values andstoring glucose variability and excursion range statistics computed frommeasurements taken over a period of time (e.g., a window of hours ordays), a more accurate characterization of the patient's changingglucose range can be determined. Using this slowly changing range, therelative position of the most recently measured glucose level comparedto the patient's history can be determined. When the most recentlymeasured glucose value is in the lower range of the patient's historicrange, then the amount of lag correction applied can be reduced by apredetermined amount as a function of the most recently measured glucosevalue and one or more slowly changing statistics collected fromhistorical sensor data of the patient. When the most recently measuredglucose value is in the middle range of the patient's historic range,the amount of lag correction applied can be set to the maximum. At thehigher range of the historic range, the amount of lag correction can bereduced as with the lower range. Thus, in this manner, the amount of lagcorrection can be reduced at the extremes of the patient's glucoseexcursions.

Embodiments of the invention are described primarily with respect tocontinuous glucose monitoring devices and systems but the presentinvention can be applied to other analytes, other analytecharacteristics, and other analyte measurement systems, as well as datafrom measurement systems that transmit sensor data from a sensor unit toanother unit such as a processing or display unit in response to arequest from the other unit. For example, other analytes that can bemonitored include, but are not limited to, acetyl choline, amylase,bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g.,CK-MB), creatine, DNA, fructosamine, glutamine, growth hormones,hormones, ketones, lactate, peroxide, prostate-specific antigen,prothrombin, RNA, thyroid stimulating hormone, and troponin. Theconcentration of drugs, such as, for example, antibiotics (e.g.,gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs ofabuse, theophylline, and warfarin, can also be monitored. In thoseembodiments that monitor more than one analyte, the analytes can bemonitored at the same or different times. In addition, in someembodiments, the present invention can be applied to non-analyte sensordata. For example, non-analyte sensor data can include temperatureestimation of a target physiological compartment that is made based onmeasuring the temperature of a nearby compartment, where the measuredtemperature lags from the temperature of the target compartment. Thepresent invention also provides numerous additional embodiments.

Some embodiments of the present invention include a programmed computersystem adapted to receive and store data from an analyte monitoringsystem. The computer system can include one or more processors forexecuting instructions or programs that implement the methods describedherein. The computer system can include memory and persistent storagedevices to store and manipulate the instructions and sensor datareceived from the analyte monitoring system. The computer system canalso include communications facilities (e.g., wireless and/or wired) toenable transfer of the sensor data from the analyte monitoring system tothe computer. The computer system can include a display and/or outputdevices for identifying dropouts in the sensor data to a user. Thecomputer system can include input devices and various other components(e.g., power supply, operating system, clock, etc.) that are typicallyfound in a conventional computer system. In some embodiments, thecomputer system is integral to the analyte monitoring system. Forexample, the computer system can be embodied as a handheld or portablereceiver unit within the analyte monitoring system.

In some embodiments, the various methods described herein for performingone or more processes, also described herein, can be embodied ascomputer programs (e.g., computer executable instructions and datastructures). These programs can be developed using an object orientedprogramming language, for example, that allows the modeling of complexsystems with modular objects to create abstractions that arerepresentative of real world, physical objects and theirinterrelationships. However, any practicable programming language and/ortechniques can be used. The software for performing the inventiveprocesses, which can be stored in a memory or storage device of thecomputer system described herein, can be developed by a person ofordinary skill in the art based upon the present disclosure and caninclude one or more computer program products. The computer programproducts can be stored on a non-transitory computer readable medium suchas a server memory, a computer network, the Internet, and/or a computerstorage device.

Turning now to FIG. 1, two glucose rates of change distributions fromtwo sensor data datasets are plotted in graph 100. The glucose rates ofchange are computed from sensor data (e.g. from an analyte measurementsystem) over all available points in a dataset. Smoothing between valuescan be performed to improve distribution uniformity. Dataset 102 istaken from measurements of patients with diabetes (PwD) and dataset 104is taken from measurements of patients without diabetes (PwoD). As canbe expected, the glucose rates of change of the PwoD are moreconcentrated in the middle area, corresponding to a slow/no rate ofchange, as compared to the distribution of the PwD data. Note that thepresent invention uses a relatively large number of glucose values(e.g., sensor data) in order to obtain a useful rate distributionmetric. In the case of self-monitored blood glucose measurement via anin vitro glucose meter, this may mean taking frequent enough fingerstick values over the course of many hours. In the case of an in vivoanalyte monitoring system that collects sensor data (such as a CGM orother type of sensor glucose monitor), a significantly shorter datacollection duration can suffice.

FIG. 2 depicts glucose rate of change distribution from the samedatasets 102, 104 shown in FIG. 1, with the distribution count (on they-axis) shown on a logarithmic scale in graph 200. Note that thedistinction in glucose variability between PwD and PwoD can be moreclearly discerned over a wider range of rates of change. Unlike FIG. 1,the transformed distribution is shaped such that a simple linear fitcould be performed on each direction of the rates of change. The slopeof this best fit line reflects the tightness of the distribution of therates of change. The steeper the absolute slope, the tighter thedistribution.

FIG. 3 illustrates the same transformed distributions as FIG. 2 but witha straight thick solid line representing the best-fit line 302 for thePwD rate distribution and a straight thick dashed line representing thebest-fit line 304 for the PwoD rate distribution in graph 300. The slopeof the best-fit line 304 taken from the transformed PwoD ratedistribution dataset 104 is much steeper than that of the best-fit line302 corresponding to the transformed PwD rate distribution dataset 102.Similarly, patients with diabetes who maintain a better glycemic controllevel will have best-fit lines with steeper slopes compared to patientswith diabetes with a poorer glycemic control level.

Turning now to FIG. 4, a flowchart depicting an example method 400according to embodiments of the present invention is provided. Sensordata is collected using an analyte measurement system (e.g., acontinuous glucose monitor) (402). In some embodiments, the sensor datais calibrated and/or scaled into glucose concentration units. Note thatthe method 400 can be applied to sensor data that is currently beingreceived from an analyte measurement system (e.g., a real-timeapplication) and/or to stored sensor data that was previously received(e.g., a retrospective application). For a real-time implementation,sensor data is collected within a moving time window of a fixed periodstarting at a point in the past up to the present time. For aretrospective implementation, stored sensor data is used in a movingtime window of a fixed period starting at a point in the past up to afuture point in time.

Once the dataset is defined, the rates of change of the data arecomputed (404). In other words, for each analyte level measurement,relative to a prior measurement, the amount of change in the analyteconcentration level per unit time is computed. Next, based on thecomputed rates of change of the data, the rate distribution of the ratesof change are computed (406). In some embodiments, the distribution ofthe rates of change are being plotted as shown in FIG. 1 describedabove. The y-axis of the distribution of the rates of change can then betransformed into a logarithmic scale (408) as shown in FIG. 2 describedabove. In some embodiments, different scales/transforms are used. Forexample, instead of a logarithmic scale, a power scale, a square-rootscale, or other scale is used to transform the plot of the distributionto taper off from zero rate in a linear fashion. The example in FIG. 2uses a base-ten, logarithmic transformation. Other base values can alsobe used. Once a transformation that renders the distribution in a linearmanner has been found and computed, a best-fit line is determined, e.g.,for both positive and negative rate sides of the transformeddistribution (410). For example, the best-fit line can be determinedusing a common “least-squares error” fit method, an orthogonal fitmethod, a method of averages, or other well-known methods. Examples ofbest-fit lines for the positive and negative rates are illustrated inFIG. 3. In some embodiments, the absolute value of the slopes of thepositive and negative rate sides of the transformed distribution arethen calculated (412). These values represent a simple objective measureof the variability of the analyte concentration and can be used invarious applications as mentioned above and described in more detailbelow.

When applied to lag correction of glucose using analyte monitoringsystem (e.g., CGM) sensor data measuring glucose in interstitial fluid,the degree of glycemic variability can be used to determine the relativebenefit of relying on the computed glucose rate of change for lagcorrection versus the risk of reduced precision caused by amplifyingnoise and other artifacts. The method 500 of determining how much lagcorrection to apply is described with reference to the flowchart of FIG.5. Using the method 400 of FIG. 4 described above, the absolute value ofthe slopes of the positive and negative rate sides of a transformed rateof change distribution are determined (502). The slopes are compared toone or more reference slopes (504). A predetermined reference slope canbe used. The units of this slope are arbitrary and are influenced by thechoice of the transformation function. For example, using a logarithmictransformation function, the base can be tuned such that the absolutevalue of the reference slope equals a convenient integer, such as 2.Other values for a predetermined reference slope can be used. In someembodiments, the slopes can additionally or alternatively be compared tothe slopes of sensor data collected from prior time periods.

If the latest slope is relatively steep, then the glucose variability isrelatively low. In this case, lag correction is relatively unnecessary(506). Conversely, if the latest slope is gentle (i.e., not steep)compared to the reference, lag correction becomes relatively moreimportant and the method proceeds to compute a correction (508).Depending on a separately determined noise metric, the amount of lagcorrection applied can vary from 0 to 100%. The noise metric is directlyrelated to the variability of the rate of change calculation, G_rate. IfG_rate is calculated from an average of first differences of glucosevalues in a pre-determined window of time, say for example, 15 minutes,then one noise metric can be calculated by taking the standard deviationof the first difference values in that window. For example, in someembodiments, the amount of lag correction to apply is determined (508)based upon the following equation:G_lag(k)=G_latest(k)+(K*τ*G_rate(k))  (Equation 1)where G_latest(k) represents the latest interstitial glucose estimate attime k, K represents a scaling factor that determines the amount of lagcorrection necessary, varying from 0 to 1. The scale K is determinedbased on two components: a comparison of the computed slope against areference slope (504) and the noise metric. For example, suppose theslope comparison generates a ratio Rs, and the noise metric generates aratio N. The slope comparison ratio Rs approaches zero for gentleslopes, and approaches one for steep slopes. The noise metric Napproaches one as the sensor signal becomes noisier, and approaches zerootherwise. Then, the scale K can be computed as a product of Rs and N.Alternatively, the scale K can be computed as the smaller of Rs or N.Tau (τ) represents the assumed time constant of lag correction, computeda priori based on population data, and G_rate(k) represents the computedglucose rate of change at time k. Thus, for an unchanging noisecharacteristic, a relatively steep glucose rate of change distributionslope results in a lower value of scale K. A relatively gentle glucoserate of change distribution slope results in a higher value of scale K.When glucose levels are not changing by a significant amount due torelatively good glycemic control, the risk of reducing precision (i.e.,increasing noise) may outweigh the benefit of increasing accuracy (i.e.,reducing lag) in the process of lag correcting in the presence of acertain level of signal noise. The calculated lag correction for eachtime k is applied to the measured interstitial fluid glucose level tomore accurately represent the patient's blood glucose level at each timek (510).

In other embodiments, the degree of glycemic variability is used toassess glycemic control for diabetes treatment evaluation, treatmentadjustment, or other purposes. For example, a method 600 of monitoringglycemic control is implemented as depicted in the flowchart of FIG. 6.Using the method 400 of FIG. 4 described above, the absolute value ofthe slopes of the positive and negative rate sides of a transformed rateof change distribution are determined (602). In some embodiments, theslopes are then compared to a record of slopes computed from historicsensor data stored from prior uses of an analyte monitoring system(604). For example, a database that stores transformed plots of rate ofchange distributions and corresponding best-fit lines for different“wears” of an analyte monitoring system sensor can be used to determinethe relative steepness and thus, the relative amount of glycemic controlof the patient compared to their past performance. A trend plot ofrelative glycemic control over time can be graphed and output by thesystem (606).

Turning to FIGS. 7A to 7C, graphs 700, 702, 704 are providedrepresenting example data collected from a patient with relatively poorglycemic control. A glucose level plot 700 over time in FIG. 7A shows arelatively high mean glucose level and indicates that a significantamount of time is spent with the glucose level changing in value. Therate of change plot 702 in FIG. 7B confirms this given the significantvariance from the zero line. The transformed plot 704 of thedistribution of the rate of change in FIG. 7C further confirms thisobservation as reflected by the slopes of the best fit lines 706, 708.

The positive rate slope 708 is steeper than the negative rate slope 706,as also indicated by the relatively faster glucose level increasescompared to the decrease towards lower glucose levels. In someembodiments, the relative steepness of the positive and negative ratedistributions can also be used to refine the patient's treatmentregimen. For example, by adjusting the lead-time between pre-prandialbolus and actual meals, the glucose level increase can be tempered down.In addition, by changing the timing and amount of correction bolus toallow for a faster initial postprandial glucose recovery followed by asmaller correction bolus later on, a softer “landing” towardsnormoglycemia can be achieved.

In addition to using glycemic variability to inform the decision whetherto apply lag correction, the glycemic range can also be useful inavoiding amplifying noise and artifacts in the sensor data. As mentionedabove, at the low glucose range, the presence of signal artifacts suchas dropouts significantly impact real-time lag correction of glucoselevels measured by the analyte monitoring system. As a patient's levelof glycemic control varies over time, their glucose range (i.e., max,min, median glucose levels) varies. When glycemic control is relativelygood, the ratio between rate calculation error and true rate istypically larger than when glycemic control is relatively poor. Thus,according to the present invention, the extent of lag correction isscaled back during critical conditions (e.g., such as the patient'sglucose level being in the low range), by using historical glucoselevels to determine the likelihood of conditions that warrant scalingback of lag correction.

Turning now to FIG. 8, a method 800 for determining an amount of lagcorrection to apply to sensor data from glucose measurement ofinterstitial fluid based on glucose level range is depicted in aflowchart. A scaling factor K is defined for lag correction that takesthe value from 0 (for no lag correction) to 1 (for full lag correction)(802). For example, let a non-lag corrected glucose value at any time tbe G_latest(t), the nominal lag correction amount be G_c(t), and thefinal lag corrected value be G_lag(t), such that:G_lag(t)=G_latest(t)+(KG_c(t))  (Equation 2)

A moving window of historical glucose sensor data is collected (804).The period of sensor data collection can be on the order of two to threedays. In some embodiments, the data includes sensor data from priorsensor wears from the same patient. A time of day probability densityfunction p(tod) of the patient's glucose level based on data in themoving window is defined using a second window size, for example, on theorder of two to three hours (806). A normalized glucose variabilityratio, Vn(t) is determined (808). An example of a normalized glucosevariability ratio is the ratio of glucose standard deviation to glucosemean within the moving window (or other similar metric) that computesvariability normalized to the overall value. Other examples ofvariability aside from standard deviation include the absolute distancebetween the upper and lower quartile of the glucose level in the movingwindow. An additional example includes the absolute distance between themedian glucose and a percentile (e.g., the tenth percentile) of theglucose in the window. Examples of an overall value aside from meanglucose include the median glucose, the average of a middle range (e.g.,the 45^(th) and 55^(th) percentile) glucose values in the window, etc.The normalized glucose variability ratio Vn(t) computed at regularintervals is stored (810). In some embodiments, the regular intervalsare on the order of every 2 to 3 days, for example. The latestnormalized glucose variability ratio Vn(t) is compared to apredetermined value Vo and the past Vn values (812). Vo is computed apriori from population data.

The value of the scaling factor K is set based upon the time of dayprobability density function p(tod) (814). At a time of day when thetime of day probability density function p(tod) predicts a highprobability of low average glucose, or when the variability from thehistoric window is very low, K is set close to 0. Otherwise, K is setclose to 1. For example, the p(tod) can be used to determine theprobability of glucose being lower than, e.g., 100 mg/dL (within a 2 to3 hour window at the current time of day). This probability can bedefined as pLow(tod), which takes on the value of 1 when the probabilityis 100%, and 0 when the probability is 0%. Then, the scaling factor forlag correction can be computed at any time (and given that time of day)using the equation:K(t,tod)=min(kLow,kN Var,kRVar)  (Equation 3)where kLow represents the gain that mitigates against historicglucose-based, predicted low glucose (kLow=1−pLow(tod)), kNVarrepresents the gain that mitigates against Vo normalized glucosevariability (kNVar=Vn(t)/Vo(t)), and kRVar represents the gain thatmitigates against past Vn normalized glucose variability(kRVar=Vn(t)/max([Vn(t−N), Vn(t−N+1), . . . , Vn(t−2), Vn(t−1)])). Inthis example, N can be on the order of 1 week. Hence, K(t,tod) is thesmallest of the three values, kLow, kNVar, kRVar, computed at any timet. The final lag corrected values are computed using Equation 2 based onthe scaling factor computed in Equation 3 (816).

Various other modifications and alterations in the structure and methodof operation of the embodiments of the present disclosure will beapparent to those skilled in the art without departing from the scopeand spirit of the present disclosure. Although the present disclosurehas been described in connection with certain embodiments, it should beunderstood that the present disclosure as claimed should not be undulylimited to such embodiments. It is intended that the following claimsdefine the scope of the present disclosure and that structures andmethods within the scope of these claims and their equivalents becovered thereby.

What is claimed is:
 1. A method comprising: determining at least one ofanalyte concentration variability and analyte concentration range;determining a lag correction value to apply to sensor datarepresentative of analyte concentration measured in interstitial fluidusing an analyte measurement system; adjusting the lag correction valueto apply to the sensor data based upon the at least one of the analyteconcentration variability and the analyte concentration range; andcomputing an analyte concentration level representative of a bloodanalyte concentration level based on the adjusted lag correction value.2. The method of claim 1, wherein the lag correction value is adjustedby an amount related to the analyte concentration variability.
 3. Themethod of claim 1, wherein the lag correction value is adjusted by anamount based on the analyte concentration range.
 4. Acomputer-implemented method, comprising: defining a scaling factor forlag correction; collecting a moving window of historical analyte sensordata; defining a probability density function of the historical analytesensor data within the moving window; determining a normalized analytevariability ratio; storing the normalized analyte variability ratiocomputed at regular intervals; comparing the normalized analytevariability ratio to a predetermined value and a number of prior values;setting a value of the scaling factor based on the probability densityfunction; and computing lag corrected values based on the scalingfactor.
 5. The method of claim 4, wherein defining the scaling factorfor lag correction includes relating a lag corrected value to thescaling factor based on the equation:G lag(t)=G latest(t)+(K G c(t)) wherein K represents the scaling factor,G latest(t) represents a non-lag corrected analyte value at time t, Gc(t) represents a nominal lag correction amount at time t, and G lag(t)represents the lag corrected value at time t.
 6. The method of claim 4,wherein defining the scaling factor for lag correction includes defininga scaling factor that ranges in value from zero to one and wherein avalue of zero corresponds to applying no lag correction and a value ofone corresponds to applying full lag correction.
 7. The method of claim4, wherein defining the probability density function of the historicalanalyte sensor data within the moving window includes using a movingwindow larger than two days.
 8. The method of claim 4, wherein definingthe probability density function of the historical analyte sensor datawithin the moving window includes using a second window having a smallerwindow size than the moving window.
 9. The method of claim 8, whereinthe second window has a window size of less than three days.
 10. Asystem for determining analyte concentration in blood based on analyteconcentration measured in interstitial fluid, the system comprising: aprocessor; and a memory coupled to the processor, the memory storingprocessor executable instructions to: define a scaling factor for lagcorrection; collect a moving window of historical analyte sensor data;define a probability density function of the historical analyte sensordata within the moving window; determine a normalized analytevariability ratio; store the normalized analyte variability ratiocomputed at regular intervals; compare the normalized analytevariability ratio to a predetermined value and a number of prior values;set a value of the scaling factor based on the probability densityfunction; and compute lag corrected values based on the scaling factor.11. The system of claim 10, wherein the instruction to define thescaling factor for lag correction includes an instruction to relate alag corrected value to the scaling factor based on the equation:G lag(t)=G latest(t)+(K G c(t)) wherein K represents the scaling factor,G latest(t) represents a non-lag corrected analyte value at time t, Gc(t) represents a nominal lag correction amount at time t, and G lag(t)represents the lag corrected value at time t.
 12. The system of claim10, wherein the instruction to define the scaling factor for lagcorrection includes an instruction to define a scaling factor thatranges in value from zero to one and wherein a value of zero correspondsto applying no lag correction and a value of one corresponds to applyingfull lag correction.
 13. The system of claim 10, wherein the instructionto define the probability density function of the historical analytesensor data within the moving window includes an instruction to use amoving window larger than two days.
 14. The system of claim 10, whereinthe instruction to define the probability density function of thehistorical analyte sensor data within the moving window includes aninstruction to use a second window having a smaller window size than themoving window.
 15. The system of claim 14, wherein the second window hasa window size of less than three days.
 16. A computer program productstored on a computer-readable medium comprising executable instructionsto: define a scaling factor for lag correction; collect a moving windowof historical analyte sensor data; define a probability density functionof the historical analyte sensor data within the moving window;determine a normalized analyte variability ratio; store the normalizedanalyte variability ratio computed at regular intervals; compare thenormalized analyte variability ratio to a predetermined value and anumber of prior values; set a value of the scaling factor based on theprobability density function; and compute lag corrected values based onthe scaling factor.
 17. The computer program product of claim 16,wherein the instruction to define the scaling factor for lag correctionincludes an instruction to relate a lag corrected value to the scalingfactor based on the equation:G lag(t)=G latest(t)+(K G c(t)) wherein K represents the scaling factor,G latest(t) represents a non-lag corrected analyte value at time t, Gc(t) represents a nominal lag correction amount at time t, and G lag(t)represents the lag corrected value at time t.
 18. The computer programproduct of claim 16, wherein the instruction to define the scalingfactor for lag correction includes an instruction to define a scalingfactor that ranges in value from zero to one and wherein a value of zerocorresponds to applying no lag correction and a value of one correspondsto applying full lag correction.
 19. The computer program product ofclaim 16, wherein the instruction to define the probability densityfunction of the historical analyte sensor data within the moving windowincludes an instruction to use a moving window larger than two days. 20.The computer program product of claim 16, wherein the instruction todefine the probability density function of the historical analyte sensordata within the moving window includes an instruction to use a secondwindow having a smaller window size than the moving window.
 21. Thecomputer program product of claim 20, wherein the second window has awindow size of less than three days.